Total Variation Regularized Tensor RPCA for Background Subtraction from Compressive Measurements

Background subtraction has been a fundamental and widely studied task in video analysis, with a wide range of applications in video surveillance, teleconferencing and 3D modeling. Recently, motivated by compressive imaging, background subtraction from compressive measurements (BSCM) is becoming an active research task in video surveillance. In this paper, we propose a novel tensor-based robust PCA (TenRPCA) approach for BSCM by decomposing video frames into backgrounds with spatial-temporal correlations and foregrounds with spatio-temporal continuity in a tensor framework. In this approach, we use 3D total variation (TV) to enhance the spatio-temporal continuity of foregrounds, and Tucker decomposition to model the spatio-temporal correlations of video background. Based on this idea, we design a basic tensor RPCA model over the video frames, dubbed as the holistic TenRPCA model (H-TenRPCA). To characterize the correlations among the groups of similar 3D patches of video background, we further design a patch-group-based tensor RPCA model (PG-TenRPCA) by joint tensor Tucker decompositions of 3D patch groups for modeling the video background. Efficient algorithms using alternating direction method of multipliers (ADMM) are developed to solve the proposed models. Extensive experiments on simulated and real-world videos demonstrate the superiority of the proposed approaches over the existing state-of-the-art approaches.Comment: To appear in IEEE TI

The Invariant Unscented Kalman Filter

International audienceThis article proposes a novel approach for nonlinear state estimation. It combines both invariant observers theory and unscented filtering principles whitout requiring any compatibility condition such as proposed in the -IUKF algorithm. The resulting algorithm, named IUKF (Invariant Unscented Kalman Filter), relies on a geometrical-based constructive method for designing filters dedicated to nonlinear state estimation problems while preserving the physical invariances and systems symmetries. Within an invariant framework, this algorithm suggests a systematic approach to determine all the symmetry- preserving terms without requiring any linearization and highlighting remarkable invariant properties. As a result, the estimated covariance matrices of the IUKF converge to quasi-constant values due to the symmetry-preserving property provided by the invariant framework. This result enables the development of less conservative robust control strategies. The designed IUKF method has been successfully applied to some relevant practical problems such as the estimation of attitude for aerial vehicles using low-cost sensors reference systems. Typical experimental results using a Parrot quadrotor are provided in this pape

Spatially resolved CO SLED of the Luminous Merger Remnant NGC 1614 with ALMA

We present high-resolution (1".0) Atacama Large Millimeter/submillimeter Array (ALMA) observations of CO (1-0) and CO (2- 1) rotational transitions toward the nearby IR-luminous merger NGC 1614 supplemented with ALMA archival data of CO (3-2), and CO (6-5) transitions. The CO (6-5) emission arises from the starburst ring (central 590 pc in radius), while the lower-$J$ CO lines are distributed over the outer disk ($\sim$ 3.3 kpc in radius). Radiative transfer and photon dominated region (PDR) modeling reveal that the starburst ring has a single warmer gas component with more intense far-ultraviolet radiation field ($n_{\rm{H_2}}$ $\sim$ 10$^{4.6}$ cm$^{-3}$, $T_{\rm{kin}}$ $\sim$ 42 K, and $G_{\rm{0}}$ $\sim$ 10$^{2.7}$) relative to the outer disk ($n_{\rm{H_2}}$ $\sim$ 10$^{5.1}$ cm$^{-3}$, $T_{\rm{kin}}$ $\sim$ 22 K, and $G_{\rm{0}}$ $\sim$ 10$^{0.9}$). A two-phase molecular interstellar medium with a warm and cold ($>$ 70 K and $\sim$ 19 K) component is also an applicable model for the starburst ring. A possible source for heating the warm gas component is mechanical heating due to stellar feedback rather than PDR. Furthermore, we find evidence for non-circular motions along the north-south optical bar in the lower-$J$ CO images, suggesting a cold gas inflow. We suggest that star formation in the starburst ring is sustained by the bar-driven cold gas inflow, and starburst activities radiatively and mechanically power the CO excitation. The absence of a bright active galactic nucleus can be explained by a scenario that cold gas accumulating on the starburst ring is exhausted as the fuel for star formation, or is launched as an outflow before being able to feed to the nucleus.Comment: 20 pages, 19 figures, 2 tables, accepted for publication in Ap

HiDAnet: RGB-D Salient Object Detection via Hierarchical Depth Awareness

RGB-D saliency detection aims to fuse multi-modal cues to accurately localize salient regions. Existing works often adopt attention modules for feature modeling, with few methods explicitly leveraging fine-grained details to merge with semantic cues. Thus, despite the auxiliary depth information, it is still challenging for existing models to distinguish objects with similar appearances but at distinct camera distances. In this paper, from a new perspective, we propose a novel Hierarchical Depth Awareness network (HiDAnet) for RGB-D saliency detection. Our motivation comes from the observation that the multi-granularity properties of geometric priors correlate well with the neural network hierarchies. To realize multi-modal and multi-level fusion, we first use a granularity-based attention scheme to strengthen the discriminatory power of RGB and depth features separately. Then we introduce a unified cross dual-attention module for multi-modal and multi-level fusion in a coarse-to-fine manner. The encoded multi-modal features are gradually aggregated into a shared decoder. Further, we exploit a multi-scale loss to take full advantage of the hierarchical information. Extensive experiments on challenging benchmark datasets demonstrate that our HiDAnet performs favorably over the state-of-the-art methods by large margins

Unsupervised Learning of Category-Specific Symmetric 3D Keypoints from Point Sets

Automatic discovery of category-specific 3D keypoints from a collection of objects of a category is a challenging problem. The difficulty is added when objects are represented by 3D point clouds, with variations in shape and semantic parts and unknown coordinate frames. We define keypoints to be category-specific, if they meaningfully represent objects’ shape and their correspondences can be simply established order-wise across all objects. This paper aims at learning such 3D keypoints, in an unsupervised manner, using a collection of misaligned 3D point clouds of objects from an unknown category. In order to do so, we model shapes defined by the keypoints, within a category, using the symmetric linear basis shapes without assuming the plane of symmetry to be known. The usage of symmetry prior leads us to learn stable keypoints suitable for higher misalignments. To the best of our knowledge, this is the first work on learning such keypoints directly from 3D point clouds for a general category. Using objects from four benchmark datasets, we demonstrate the quality of our learned keypoints by quantitative and qualitative evaluations. Our experiments also show that the keypoints discovered by our method are geometrically and semantically consistent

The Planetary Mass Companion 2MASS1207-3932 B: Temperature, Mass and Evidence for an Edge-On Disk

We present J-band imaging and H+K-band low-resolution spectroscopy of 2MASS1207-3932 AB, obtained with VLT NACO. For the putative planetary mass secondary, we find J = 20.0+/-0.2 mag. The HK spectra of both components imply low gravity, and a dusty atmosphere for the secondary. Comparisons to synthetic spectra yield Teff_A ~ 2550+/-150K, and Teff_B ~ 1600+/-100K, consistent with their late-M and mid-to-late L types. For these Teff, and an age of 5-10 Myrs, evolutionary models imply M_A ~ 24+/-6 M_Jup and M_B ~ 8+/-2 M_Jup. Independent comparisons of these models to the observed colors, spanning ~I to L', also yield the same masses and temperatures. Our primary mass agrees with other recent analyses; however, our secondary mass, while still in the planetary regime, is 2-3 times larger than claimed previously. This discrepancy can be traced to the luminosities: while the absolute photometry and Mbol of the primary agree with theoretical predictions, the secondary is ~ 2.5+/-0.5 mag fainter than expected in all bands from I to L' and in Mbol. This accounts for the much lower secondary mass (and temperature) derived earlier. We argue that this effect is highly unlikely to result from a variety of model-related problems, and is instead real. This conclusion is bolstered by the absence of any luminosity problems in either the primary, or in AB Pic B which we also analyse. We therefore suggest grey extinction in 2M1207B, due to occlusion by an edge-on circum-secondary disk. This is consistent with the observed properties of edge-on disks around T Tauri stars, and with the known presence of a high-inclination evolved disk around the primary. Finally, the system's implied mass ratio of ~0.3 suggests a binary-like formation scenario. (abridged)Comment: Accepted by The Astrophysical Journal, 43 pages text + 16 figs + 1 tabl

A convolutional neural network based Chinese text detection algorithm via text structure modeling

Text detection in natural scene environment plays an important role in many computer vision applications. While existing text detection methods are focused on English characters, there is strong application demands on text detection in other languages, such as Chinese. As Chinese characters are much more complex than English characters, innovative and more efficient text detection techniques are required for Chinese texts. In this paper, we present a novel text detection algorithm for Chinese characters based on a specific designed convolutional neural network (CNN). The CNN model contains a text structure component detector layer, a spatial pyramid layer and a multi-input-layer deep belief network (DBN). The CNN is pretrained via a convolutional sparse auto-encoder (CSAE) in an unsupervised way, which is specifically designed for extracting complex features from Chinese characters. In particular, the text structure component detectors enhance the accuracy and uniqueness of feature descriptors by extracting multiple text structure components in various ways. The spatial pyramid layer is then introduced to enhance the scale invariability of the CNN model for detecting texts in multiple scales. Finally, the multi-input-layer DBN is used as the fully connected layers in the CNN model to ensure that features from multiple scales are comparable. A multilingual text detection dataset, in which texts in Chinese, English and digits are labeled separately, is set up to evaluate the proposed text detection algorithm. The proposed algorithm shows a significant 10% performance improvement over the baseline CNN algorithms. In addition the proposed algorithm is evaluated over a public multilingual image benchmark and achieves state-of-the-art results for text detection under multiple languages. Furthermore a simplified version of the proposed algorithm with only general components is compared to existing general text detection algorithms on the ICDAR 2011 and 2013 datasets, showing comparable detection performance to the existing algorithms